Classifications

• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L63/00—Network architectures or network communication protocols for network security
  • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
  • H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
  • H04L63/1425—Traffic logging, e.g. anomaly detection
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
  • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
  • G06F21/55—Detecting local intrusion or implementing counter-measures
  • G06F21/552—Detecting local intrusion or implementing counter-measures involving long-term monitoring or reporting
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
  • G06F16/28—Databases characterised by their database models, e.g. relational or object models
  • G06F16/284—Relational databases
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L63/00—Network architectures or network communication protocols for network security
  • H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
  • H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F11/00—Error detection; Error correction; Monitoring
  • G06F11/30—Monitoring
  • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
  • G06F11/3466—Performance evaluation by tracing or monitoring
  • G06F11/3476—Data logging
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
  • G06F2201/86—Event-based monitoring
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
  • G06F2221/03—Indexing scheme relating to G06F21/50, monitoring users, programs or devices to maintain the integrity of platforms
  • G06F2221/034—Test or assess a computer or a system

Definitions

• This invention pertains in general to security information/event management (SIM or SIEM) and in particular to storing security information/events efficiently while supporting querying.
• SIM security information/event management
• SIEM SIEM
• the field of security information/event management is generally concerned with 1) collecting data from networks and networked devices that reflects network activity and/or operation of the devices and 2) analyzing the data to enhance security. For example, the data can be analyzed to identify an attack on the network or a networked device and determine which user or machine is responsible. If the attack is ongoing, a countermeasure can be performed to thwart the attack or mitigate the damage caused by the attack.
• the data that is collected usually originates in a message (such as an event, alert, or alarm) or an entry in a log file, which is generated by a networked device.
• Exemplary networked devices include firewalls, intrusion detection systems, and servers.
• Each message or log file entry (“event”) is stored for future use.
• Stored events can be organized in a variety of ways. Each organizational method has its own advantages and disadvantages when it comes to writing event data, searching event data, and deleting event data.
• Each event includes an attribute called event receipt time. Since the value of the event receipt time attribute is frequently used for searching, store events based on their event receipt times. For example, create one file for each minute of the day. In order to store an event, determine that event's event receipt time. Append the event to the file that corresponds to that minute of event receipt time.
• When subsequent events arrive their event receipt times will always increase monotonically. This means that writing the subsequent event data will require only append operations.
• a logging system stores security information/events efficiently while supporting querying for different event attributes.
• the logging system can be used in conjunction with a security information/event management (SIEM) system.
• Log data which can be generated by various sources (including devices and applications), can be in any format. Log data is comprised of one or more data instances called "events."
• An event can be, for example, an entry in a log file, an entry in a syslog server, an alert, an alarm, a network packet, an email, or a notification page. In general, an event is generated once and does not change afterwards.
• the logging system includes an event receiver, a storage manager, and a communication mechanism.
• the event receiver receives log data, processes the log data, and outputs a column-based data "chunk.”
• the event receiver includes a control system, a set of buffers, and a metadata structure.
• the control system controls operation of the event receiver.
• the set of buffers stores one or more events. If different events include the same types of fields, then the events can be organized in a table. Each row of the table would represent a different event, and each column of the table would represent a different field.
• Each buffer is associated with a particular field and includes values from that field ("attributes") from one or more events.
• the metadata structure stores metadata about the contents of the set of buffers.
• the metadata includes a unique identifier associated with the event receiver, the number of events in the set of buffers, and, for each of one or more "fields of interest," a minimum value and a maximum value that reflect the range of values of that field over all of the events in the set of buffers.
• the metadata structure acts as a search index when querying event data.
• the storage manager receives column-based data chunks and stores them so that they can be queried.
• the storage manager includes a control system, a datafiles table, a chunks table, and one or more datafiles.
• the control system controls operation of the storage manager.
• the datafiles table stores information about the one or more datafiles. In one embodiment, this information includes, for each datafile, a unique identifier associated with the datafile and the location of the datafile.
• the chunks table stores information about the one or more column-based chunks that are stored in the storage manager (specifically, stored in the one or more datafiles). In one embodiment, this information includes, for each column-based chunk, the metadata stored in the chunk and the location of the chunk.
• a datafile stores multiple chunks.
• the communication mechanism communicatively couples the event receiver and the storage manager.
• the event receiver and the storage manager jointly perform a method for storing log data.
• the event receiver receives log data.
• the event receiver control system separates the log data into one or more events and determines when each event was received by the event receiver.
• the control system stores in the set of buffers the field values of the events and, for each event, a time/date stamp that reflects when the event was received.
• the control system also updates the metadata structure.
• the control system generates column-based data chunks based on the metadata structure and the contents of the set of buffers (one column-based chunk for each buffer).
• a column-based chunk includes the metadata structure and a compressed version of the contents of the buffer.
• the set of buffers and the metadata structure are re-initialized, thereby flushing the set of buffers.
• the control system sends the column-based chunks to the storage manager.
• the storage manager receives the chunks, stores the chunks in a datafile, and updates the chunks table. [0013]
• the storage manager performs a method for reclaiming storage. The oldest datafile associated with a particular retention policy is identified. Information regarding all of the column- based chunks contained in the identified datafile is removed from the chunks table. The entry in the datafiles tables that represents the identified datafile is deleted. A new entry is created in the datafiles table. The newly reclaimed datafile is added to the list of available pre-allocated datafiles and is ready to receive new chunks.
• a query is represented as an expression that can be evaluated against an event.
• the expression includes one or more search terms.
• a search term concerns the contents of an event, specifically, a particular field and the value of that field.
• data chunks are first filtered based on "field of interest" information (as stored in a chunk's metadata).
• the remaining chunks are then filtered based on field values (as stored in a chunk's "payload").
• the events that satisfy the query are assembled.
• 11/966,078 (“the '078 Application”) describes storing event data using row-based chunks.
• a third type of event storage uses both row-based chunks and column-based chunks. This type of event storage stores an event twice - once using a row-based chunk and once using one or more column-based chunks. For example, a set of events would be stored as one row-based chunk. The field values of those events would also be stored as column-based chunks (one column-based chunk for each field).
• FIG. 1 is a block diagram illustrating an environment having a security information/event management system, according to one embodiment.
• FIG. 2 is block diagram illustrating a computer for acting as a logging system of a security information/event management system, according to one embodiment.
• FIG. 3 is a block diagram illustrating a logging system of a security information/event management system, according to one embodiment.
• FIG. 4 is a flowchart illustrating a method for storing log data, according to one embodiment.
• FIG. 5 is a flowchart illustrating a method for reclaiming storage, according to one embodiment.
• FIG. 6 is a flowchart illustrating a method for querying, according to one embodiment.
• the figures depict an embodiment for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein.
• Described herein is a computer-based system for collecting data from disparate devices across a computer network, normalizing the data to a common schema, and consolidating the normalized data.
• the data (“events”) can then be monitored, analyzed, and used for investigation and remediation in a centralized view.
• Events can be cross-correlated with rules to create meta-events. Correlation includes, for example, discovering the relationships between events, inferring the significance of those relationships (e.g., by generating meta-events), prioritizing the events and meta- events, and providing a framework for taking action.
• the system (one embodiment of which is manifest as computer software) enables aggregation, correlation, detection, and investigative tracking of suspicious network activities.
• the system also supports response management, ad-hoc query resolution, reporting and replay for forensic analysis, and graphical visualization of network threats and activity.
• these examples should not be read to limit the broader spirit and scope of the present invention.
• the examples presented herein describe distributed agents, managers and consoles, which are but one embodiment of the present invention.
• the general concepts and reach of the present invention are much broader and may extend to any computer-based or network-based security system.
• examples of the messages that may be passed to and from the components of the system and the data schemas that may be used by components of the system are given in an attempt to further describe the present invention, but are not meant to be all-inclusive examples and should not be regarded as such.
• one embodiment of the present invention is instantiated in computer software, that is, machine readable instructions, which, when executed by one or more computer processors/systems, instruct the processors/systems to perform the designated actions.
• Such computer software may be resident in one or more machine readable storage media, such as hard drives, CD- ROMs, DVD-ROMs, read-only memory, read-write memory and so on.
• Such software may be distributed on one or more of these media, or may be made available for download across one or more computer networks (e.g., the Internet).
• the computer programming, rendering and processing techniques discussed herein are simply examples of the types of programming, rendering and processing techniques that may be used to implement aspects of the present invention. These examples should in no way limit the present invention, which is best understood with reference to the claims that follow this description.
• SIEM Security Information/Event Management
• FIG. 1 is a block diagram illustrating an environment having a security information/event management system, according to one embodiment.
• FIG. 1 includes a security information/event management (SIEM) system 100 and one or more data sources 110.
• a data source 110 is a network node, which can be a device or a software application.
• Exemplary data sources 110 include intrusion detection systems (IDSs), intrusion prevention systems (IPSs), vulnerability assessment tools, firewalls, anti-virus tools, anti-spam tools, encryption tools, application audit logs, and physical security logs.
• IDSs intrusion detection systems
• IPSs intrusion prevention systems
• vulnerability assessment tools firewalls, anti-virus tools, anti-spam tools, encryption tools, application audit logs, and physical security logs.
• Types of data sources 110 include security detection and proxy systems, access and policy controls, core service logs and log consolidators, network hardware, encryption devices, and physical security.
• Exemplary security detection and proxy systems include IDSs, IPSs, multipurpose security appliances, vulnerability assessment and management, anti-virus, honeypots, threat response technology, and network monitoring.
• Exemplary access and policy control systems include access and identity management, virtual private networks (VPNs), caching engines, firewalls, and security policy management.
• Exemplary core service logs and log consolidators include operating system logs, database audit logs, application logs, log consolidators, web server logs, and management consoles.
• Exemplary network hardware includes routers and switches.
• Exemplary encryption devices include data security and integrity.
• Exemplary physical security systems include card-key readers, biometrics, burglar alarms, and fire alarms.
• the SIEM system 100 includes one or more agents 120, one or more managers 130, one or more databases 140, one or more online archives 150, one or more user interfaces 160, and one or more logging systems 170. In some embodiments, these modules are combined in a single platform or distributed in two, three, or more platforms (such as in FIG. 1). The use of this multi-tier architecture supports scalability as a computer network or system grows.
• the SIEM system 100 is further described in U.S. Patent No. 7,376,969, issued May 20, 2008, which is hereby incorporated by reference herein in its entirety.
• An agent 120 provides an interface to a data source 110. Specifically, the agent 120 collects data ("raw events") from a data source 110, processes the data, and sends the processed data ("events") to a manager 130.
• the agent 120 can operate anywhere, such as at a separate device communicating via a protocol such as simple network management protocol (SNMP) traps, at a consolidation point within the network, or at the data source 110.
• SNMP simple network management protocol
• the agent 120 can be co-hosted on the device that hosts the data source.
• the agent 120 is the Connector product from ArcSight, Inc. of Cupertino, CA.
• Processing can include normalization, aggregation, and filtering.
• Normalization can involve normalizing values (such as severity, priority, and time zone) into a common format and/or normalizing a data structure into a common schema. Events can be categorized using a common, human-readable format. This format makes it easier for users to understand the events and makes it easier to analyze the events using filters, rules, reports, and data monitors.
• the common format is the Common Event Format (CEF) log management standard from ArcSight, Inc. Normalization is further described in U.S. Application No. 10/308,941, filed December 2, 2002, which is hereby incorporated by reference herein in its entirety.
• Aggregation and filtering reduce the volume of events sent to the manager 130, which saves network bandwidth and storage space, increases the manager's efficiency and accuracy, and reduces event processing time. Aggregation is further described in U.S. Application No. 10/308,584, filed December 2, 2002, and U.S. Application No. 10/975,962, filed October 27, 2004, which are hereby incorporated by reference herein in their entirety.
• the agent 120 sends events to the manager 130 in batches based on the expiration of a time period or based on a threshold number of events being reached. Batching events for transmission to the manager 130 is further described in U.S. Patent No. 7,219,239, issued May 15, 2007, which is hereby incorporated by reference herein in its entirety.
• the agent 120 can also send commands to the data source 110 and/or execute commands on the local host, such as instructing a scanner to run a scan. These actions can be executed manually or through automated actions from rules and data monitors. Command support is further described in U.S. Application No. 10/308,417, filed December 2, 2002, which is hereby incorporated by reference herein in its entirety.
• the agent 120 can also add information to the data that it has collected, such as by looking up an Internet Protocol (IP) address and/or hostname in order to resolve IP/hostname lookup at the manager 130.
• IP Internet Protocol
• the agent 120 is configured via an associated configuration file (not shown).
• the agent 120 can include one or more software modules including a normalizing component, a time correction component, an aggregation component, a batching component, a resolver component, a transport component, and/or additional components. These components can be activated and/or deactivated through appropriate commands in the configuration file.
• the agent 120 is registered to a manager 130 and configured with characteristics based on its data source 110 and desired behavior.
• the agent 120 is further configurable through both manual and automated processes. For example, the manager 130 can send to the agent 120 a command or configuration update.
• Agent components are further described in U.S. Application No. 10/308,548, filed December 2, 2002, which is hereby incorporated by reference herein in its entirety.
• a manager 130 provides analysis capabilities, case management workflow capabilities, and services capabilities. Communications between the manager 130 and an agent 120 can be bidirectional (e.g., to enable the manager 130 to transmit a command to the platform hosting the agent 120) and encrypted. In some installations, the manager 130 can act as a concentrator for multiple agents 120 and can forward information to other managers 130 (e.g., managers deployed at a corporate headquarters). To perform its tasks, the manager 130 uses a variety of filters, rules, reports, data monitors, dashboards, and network models. In one embodiment, the manager 130 is a Java-based server such as the Enterprise Security Manager (ESM) product from ArcSight, Inc. [0036] Analysis can include detection, correlation, and escalation.
• ESM Enterprise Security Manager
• the manager 130 cross-correlates the events received from the agents 120 using a rules engine (not shown), which evaluates each event with network model and vulnerability information to develop real-time threat summaries. Correlation is further described in U.S. Application No. 10/308,767, filed December 2, 2002, which is hereby incorporated by reference herein in its entirety. Regarding case management, the manager 130 can maintain reports regarding the status of security incidents and their resolution. Incident reports are further described in U.S. Application No. 10/713,471, filed November 14, 2003, which is hereby incorporated by reference herein in its entirety. Services can include administration, notification, and reporting. The manager 130 can also provide access to a knowledge base. Additional manager capabilities are described in U.S. Application No.
• the manager 130 As events are received by the manager 130, they are stored in a database 140. Storing the events enables them to be used later for analysis and reference.
• the database 140 is a relational database management system such as a database from Oracle Corporation of Redwood Shores, CA.
• the database 140 stores data in partitions, which are chronological slices of the database. For example, one new partition is created each day to store that day's events.
• a partition can be compressed and stored in an online archive 150 for later retrieval. Partition management is further described in U.S. Application No. 10/839,563, filed May 4, 2004, which is hereby incorporated by reference herein in its entirety.
• partition management is provided by the SmartStorage archiving and retrieval component of the Security Lifecycle Information Management (SLIM) product from ArcSight, Inc.
• SLIM Security Lifecycle Information Management
• a user interacts with the manager 130 via a user interface 160.
• the user interface 160 enables the user to navigate the features and functions of the manager 130.
• a single manager 130 can support multiple user interface instances. The features and functions that are available to the user can depend on the user's role and permissions and/or the manager's configuration.
• access control lists enable multiple security professionals to use the same manager 130 and database 140 but each professional has his own views, correlation rules, alerts, reports, and knowledge bases appropriate to his responsibilities. Communication between the manager 130 and the user interface 160 is bi-directional and can be encrypted.
• a workstation-based interface there are two types of user interfaces 160: a workstation-based interface and a web browser-based interface.
• the workstation interface is a standalone software application that is intended for use by full-time security staff in a Security Operations Center (SOC) or similar security monitoring environment.
• the workstation interface includes an authoring tool for creating and modifying filters, rules, reports, pattern discovery, dashboards, and data monitors.
• the workstation interface also enables a user to administer users, database partitions, and workflow (e.g., incident investigation and reporting).
• the workstation interface enables a user to perform routine monitoring, build complex correlation and long sequence rules, and perform routine administrative functions.
• the workstation interface is the ESM Console product from ArcSight, Inc.
• the user interface is further described in U.S. Application No. 10/308,418, filed December 2, 2002, and U.S. Patent No. 7,333,999, issued February 19, 2008, which are hereby incorporated by reference herein in their entirety.
• the web interface is an independent and remotely installable web server that provides a secure interface with the manager 130 for web browser clients.
• the web interface is intended for use as a streamlined interface for customers of Managed Service Security Providers (MSSPs), SOC operators, and users who need to access the manager 130 from outside the protected network. Because the web server can be installed at a location remote from the manager 130, the web server can operate outside the firewall that protects the manager 130.
• the web interface provides event monitoring and drill-down capabilities. In one embodiment, as a security feature, the web interface does not enable authoring or administrative functions.
• the web interface is the ArcSight Web product from ArcSight, Inc.
• a logging system 170 is an event data storage appliance that is optimized for extremely high event throughput.
• the logging system 170 stores security events (sometimes referred to as "log data").
• the security events are stored in compressed form.
• the logging system 170 can retrieve these events on demand and restore them to their original, unmodified form for forensics-quality data.
• Multiple logging systems 170 can work together to scale up to support high sustained input rates when storing events.
• Event queries can be distributed across a peer network of logging systems 170.
• a user can configure the logging system 170 via a user interface (not shown).
• the logging system 170 is the Logger product from ArcSight, Inc.
• the logging system 170 can receive both processed events (e.g., events adhering to the Common Event Format) and raw events.
• raw events are received directly from data sources 110 (such as syslog messages and log files), and processed events are received from agents 120 or managers 130.
• the logging system 170 can also send both raw events and processed events.
• raw events are sent as syslog messages (to any device; not shown), and processed events are sent to the manager 130.
• the logging system 170 will be further described below.
• the SIEM system 100 can support a centralized or decentralized environment. This is useful because an organization may want to implement a single instance of the SIEM system 100 and use an access control list to partition users. Alternatively, the organization may choose to deploy separate SIEM systems 100 for each of a number of groups and consolidate the results at a "master" level. Such a deployment can also achieve a "follow-the-sun” arrangement where geographically dispersed peer groups collaborate with each other by passing primary oversight responsibility to the group currently working standard business hours. SIEM systems 100 can also be deployed in a corporate hierarchy where business divisions work separately and support a rollup to a centralized management function.
• Log data can be generated by various sources, including both devices and applications. These sources include, for example, the data sources 110 described above as well as network systems, computers, operating systems, anti- virus systems, databases, physical infrastructure, identity management systems, directory services, system health information systems, web traffic, legacy systems, proprietary systems, mainframes, mainframe applications, security systems, physical devices, and SIEM sources (such as agents 120 and managers 130).
• sources include, for example, the data sources 110 described above as well as network systems, computers, operating systems, anti- virus systems, databases, physical infrastructure, identity management systems, directory services, system health information systems, web traffic, legacy systems, proprietary systems, mainframes, mainframe applications, security systems, physical devices, and SIEM sources (such as agents 120 and managers 130).
• a system can obtain log data in many ways. For example, log data can be received (e.g., according to the syslog protocol).
• log data can be accessed (e.g., by reading a file that is stored locally or remotely).
• Other methods include, for example, Open Database Connectivity (ODBC), Simple Network Management Protocol (SNMP) traps, NetFlow, and proprietary Application Programming Interfaces (APIs).
• Log data can also be input by a user (e.g., using a command line interface (CLI)).
• ODBC Open Database Connectivity
• SNMP Simple Network Management Protocol
• APIs Application Programming Interfaces
• Log data can also be input by a user (e.g., using a command line interface (CLI)).
• CLI command line interface
• Log data can be in any format.
• One such format is, for example, Common Event Format (described above).
• Other formats are, for example, specific to the data sources 1 10 that generated the log data.
• Log data is comprised of one or more data instances called "events.”
• An event can be, for example, an entry in a log file, an entry in a syslog server, an alert, an alarm, a network packet, an email, or a notification page. In general, an event is generated once and does not change afterwards.
• an event includes implicit meta-data and a message. Implicit metadata can include information about, for example, the device or application that generated the event ("event source") and when the event was received from the event source ("receipt time").
• the receipt time is a date/time stamp
• the event source is a network endpoint identifier (e.g., an IP address or Media Access Control (MAC) address) and/or a description of the source, possibly including information about the product's vendor and version.
• the message represents what was received from the event source and can be in any form (binary data, alphanumeric data, etc.).
• the message is free-form text that describes a noteworthy scenario or change.
• the message also includes explicit metadata. Explicit meta-data is obtained, for example, by parsing the message.
• the event When an event source generates an event, the event usually includes information that indicates when the event occurred ("event occurrence time").
• the event occurrence time which is usually a date/time stamp, is an example of explicit meta-data and is frequently used for analysis. Different event sources often produce non-uniform explicit meta-data (e.g., priority or criticality of event, devices/applications/users affected by event, and which user triggered event).
• an implicit timestamp generated by an event receiver when it received the event is treated as the original occurrence timestamp. As an event is processed and potentially forwarded through various systems, each system usually has an implicit notation of event receipt time.
• an event represents a data structure that includes one or more fields, where each field can contain a value (sometimes referred to as an "attribute").
• the size of this data structure usually falls within the range of 100 bytes to 10 kilobytes.
• the events can be organized in a table. Each row of the table would represent a different event, and each column of the table would represent a different field.
• the event data can be stored in a database using two architectures: row store and column store.
• row store architecture storage is record- (row-) oriented.
• the attributes (field values) of a record (or tuple) are placed contiguously in storage.
• DBMS database management system
• WOS write-optimized system
• column store architecture storage is field- (column-) oriented.
• the values stored in one column, across multiple records, are placed contiguously in storage.
• a DBMS needs to read the values of only those columns that are required for processing a given query and can avoid loading into memory irrelevant field values (attributes).
• high performance ad-hoc querying is achieved, and a DBMS with a column store architecture is called a read-optimized system
• U.S. Application No. 11/966,078 (“the '078 Application”) describes storing event data using row-based "chunks.”
• the '078 Application describes a logging system that includes an event receiver and a storage manager.
• the receiver receives log data, processes it, and outputs a row-based data "chunk.”
• the manager receives the row-based data chunk and stores it so that it can be queried.
• the receiver includes buffers that store events and a metadata structure that stores information about the contents of the buffers.
• the metadata includes a unique identifier associated with the receiver, the number of events in the buffers, and, for each "field of interest," a minimum value and a maximum value that reflect the range of values of that field over all of the events in the buffers.
• a chunk includes the metadata structure and a compressed version of the contents of the buffers.
• the metadata structure acts as a search index when querying event data.
• the logging system can be used in conjunction with a security information/event management (SIEM) system.
• SIEM security information/event management
• a chunk includes the contents of the event receiver buffers (in compressed form), and the buffers contain one or more events. Thus, a chunk contains one or more events. Since an event can be thought of as a row of a table, a chunk can be thought of as containing one or more rows of a table. In other words, the chunks described in the '078 Application follow a row store architecture.
• the table would be represented as multiple column-based chunks (one for each column of the table).
• the present application describes storing event data using column-based chunks such that the chunks follow a column store architecture.
• the present application also describes storing event data using a combination of row-based chunks and column-based chunks. Pure column-based storage will be described first, followed by the combination row-based and column-based storage.
• FIG. 2 is a high-level block diagram of a computer 200 for acting as a logging system 170 of a security information/event management (SIEM) system 100 according to one embodiment. Illustrated are at least one processor 202 coupled to a bus 204. Also coupled to the bus 204 are a memory 206, a storage device 208, a keyboard 210, a graphics adapter 212, a pointing device 214, and a network adapter 216. In one embodiment, the functionality of the bus 204 is provided by an interconnecting chipset. A display 218 is coupled to the graphics adapter 212.
• SIEM security information/event management
• the storage device 208 is any device capable of holding data, like a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device.
• the memory 206 holds instructions and data used by the processor 202.
• the pointing device 214 may be a mouse, track ball, or other type of pointing device, and is used in combination with the keyboard 210 to input data into the computer 200.
• the graphics adapter 212 displays images and other information on the display 218.
• the network adapter 216 couples the computer 200 to a local or wide area network.
• a computer 200 can have different and/or other components than those shown in FIG. 2. In addition, the computer 200 can lack certain illustrated components.
• FIG. 3 is a block diagram illustrating a logging system 170 of a security information/event management (SIEM) system 100, according to one embodiment.
• the logging system 170 includes an event receiver 310, a storage manager 320, and a communication mechanism 330. Although only one event receiver 310 is shown for clarity, the system 170 can support a large number of concurrent sessions with many event receivers 310.
• each event receiver 310 is associated with a unique identifier.
• the event receiver 310 receives log data 340, processes the log data 340, and outputs a data "chunk" 350.
• the event receiver 310 includes a control system 355, a set of one or more buffers 360, and a metadata structure 365.
• the control system 355 is communicatively coupled to the set of one or more buffers 360 and the metadata structure 365.
• control system 355 controls operation of the event receiver 310 and is further described below with reference to FIG. 4.
• Each buffer 360 stores information regarding one or more events.
• a buffer's size is fixed but the size itself is configurable. Recall that if different events include the same types of fields, then the events can be organized in a table. Each row of the table would represent a different event, and each column of the table would represent a different field.
• each buffer 360 is associated with a particular field and includes values from that field ("attributes") from one or more events.
• each buffer 360 also includes an identifier (“IndexID”) that indicates which field is associated with the buffer.
• an event includes a field called SourceIP Address whose value reflects the IP address of the device that initiated the action represented by the event.
• a buffer 360 associated with the SourceIP Address field would contain one or more IP addresses (one IP address for each event that was received and processed by the event receiver 310 as part of the log data 340).
• the buffer 360 might also contain an IndexID value of "100," which indicates the SourceIP Address field.
• the set of buffers 360 includes one buffer for each event field. When an event is received, each field value is parsed out and stored in the appropriate buffer (described below). Eventually, each buffer is stored as a separate column-based chunk (discussed below).
• each column of the event "table” is stored as a different (column-based) chunk.
• Each column- based chunk would represent a column in the table (i.e., a set of values for the same field in multiple events). Rather than representing the table as one big row-based chunk that contained all of the rows (events), the table would be represented as multiple column-based chunks (one for each column of the table).
• the set of buffers also includes a ReceiptTime buffer that stores, for each event, a time/date stamp that reflects when the event was received by the event receiver 310.
• the set of buffers also includes a buffer that stores, for each event, a "derived" value that is determined based on the values stored in one or more fields of an event.
• the metadata structure 365 stores metadata about the contents of the set of buffers 360.
• this metadata includes the unique identifier associated with the event receiver 310 that received the events, the number of events in the set of buffers, and, for each of one or more "fields of interest," a minimum value and a maximum value that reflect the range of values of that field over all of the events in the set of buffers.
• the metadata structure 365 acts as a search index when querying event data (described below).
• an event includes a field called OccurrenceTime whose value reflects the time that the event occurred.
• OccurrenceTime a field of interest
• the metadata structure 365 would include a minimum value for OccurrenceTime and a maximum value for OccurrenceTime.
• the minimum value of OccurrenceTime would be the OccurrenceTime for the event in the set of buffers 360 that occurred first.
• the maximum value of OccurrenceTime would be the OccurrenceTime for the event in the set of buffers 360 that occurred last.
• ReceiptTime is also a field of interest.
• the metadata structure 365 also stores a minimum value and a maximum value that reflect the range of values of receipt times over all of the events in the set of buffers.
• the minimum value of ReceiptTime would be the ReceiptTime for the event in the set of buffers 360 that was received first.
• the maximum value of ReceiptTime would be the ReceiptTime for the event in the set of buffers 360 that was received last.
• only the minimum value of ReceiptTime is stored.
• the maximum value of ReceiptTime is not stored; this decreases storage requirements. If a buffer 360 is flushed often (which happens when a chunk is generated, described below), the maximum value of ReceiptTime will be close to the minimum value of ReceiptTime (e.g., one second later).
• a field of interest is not an event field per se. Instead, it is a "derived" value that is determined based on the values stored in one or more fields of an event.
• the storage manager 320 receives data chunks 350 and stores them so that they can be queried.
• the storage manager 320 includes a control system 370, a datafiles table 375, a chunks table 380, and one or more dataf ⁇ les 385.
• the control system 370 is communicatively coupled to the datafiles table 375, the chunks table 380, and the one or more datafiles 385.
• the control system 370 controls operation of the storage manager 320 and is further described below with reference to FIG. 4.
• the datafiles table 375 stores information about the one or more datafiles 385.
• each entry in the datafiles table 375 represents one datafile 385 for which space has been allocated, and the entry includes a unique identifier associated with the datafile and the location of the datafile (e.g., a file system, a path therein, and a file name).
• a datafile 385 listed in the datafiles table 375 may or may not contain data (e.g., chunks 350).
• the datafiles table 375 is stored, for example, in a database (not shown). In one embodiment, datafiles 385 are allocated before they are needed.
• the chunks table 380 stores information about the one or more chunks 350 that are stored in the storage manager 320 (specifically, stored in the one or more datafiles 385). In one embodiment, this information includes, for each chunk 350, the metadata stored in the chunk (described below) and the location of the chunk (e.g., the unique identifier associated with the datafile that stores the chunk and the location within the datafile where the chunk is stored (e.g., as an offset)).
• the chunks table 380 is stored, for example, in a database (not shown).
• a datafile 385 stores multiple chunks 350. In one embodiment, all datafiles are the same size (e.g., 1 gigabyte) and are organized in time order.
• the datafile 385 is stored, for example, on a raw disk or in a data storage system such as a file system (not shown). If the datafile 385 is stored on a raw disk, data can be accessed faster, since additional layers of indirection are not required. Also, security can be increased.
• the communication mechanism 330 communicatively couples the event receiver 310 and the storage manager 320.
• the communication mechanism 330 includes a partially- public or wholly-public network such as the Internet.
• the communication mechanism 330 includes a private network or one or more distinct or logical private networks (e.g., virtual private networks or local area networks). Communication links to and from the communication mechanism 330 can be wired or wireless (e.g., terrestrial- or satellite-based transceivers).
• the communication mechanism 330 is a packet-switched network such as an IP -based wide or metropolitan area network that uses the Ethernet protocol.
• the communication mechanism 330 is local to a single computer system (e.g., if a portion of the event receiver 310 and a portion of the storage manager 320 are executing on the same device). In this embodiment, the communication mechanism 330 is implemented, for example, through a local, software-only loopback device. For example, the data is copied to various locations in memory, and communication occurs via an API. [0081] In yet another embodiment, the communication mechanism 330 is local to a single process (e.g., if a portion of the event receiver 310 and a portion of the storage manager 320 are executing on the same device and in the same process). In this embodiment, the communication mechanism 330 is implemented, for example, through shared memory and/or pointers thereto.
• FIG. 4 is a flowchart illustrating a method for storing log data, according to one embodiment of the invention.
• the method 400 of FIG. 4 is performed jointly by the event receiver 310 (e.g., its control system 355) and the storage manager 320 (e.g., its control system 370).
• the set of buffers 360 and the metadata structure 365 are initialized.
• the control system 355 stores, in each buffer, the appropriate IndexID.
• the control system 355 also stores in the metadata structure 365 the unique identifier associated with the event receiver 310.
• the method 400 begins when the event receiver 310 receives 410 log data 340.
• the log data 340 is received in the form of a stream.
• the control system 355 separates 420 the log data into one or more events and determines
• the control system 355 parses 430 the events into their field values and stores the field values and receipt times in the appropriate buffers.
• the control system 355 also updates 430 the metadata structure 365. For example, the number of events in the buffer will have increased. The minimum and maximum values for the field(s) of interest may also need to be updated.
• data write operations and metadata write operations are synchronized in order to avoid possible inconsistency if a system crash occurs. For example, a transactional database system is used so that if field values are stored in the buffer 360, the metadata structure 365 is guaranteed to be updated accordingly, even if the underlying system crashes in between the two steps.
• the control system 355 generates 440 data chunks 350 based on the metadata structure 365 and the contents of the buffers 360. Specifically, one chunk is generated for each buffer. Different chunks can have different sizes. Chunk sizes can differ due to, for example, the type of field values stored in a chunk (and the compression algorithm applied to them, discussed below), and the type of trigger that caused the chunk to be generated (also discussed below). In one embodiment, a maximum chunk size can be specified.
• each chunk includes the metadata structure 365, the contents of the associated buffer, a chunk identifier (ChunkID), a stripe identifier (StripelD), and a set of index location identifiers (IndexLocationIDs).
• ChunkID chunk identifier
• StripelD stripe identifier
• IndexLocationIDs index location identifiers
• the ChunkID uniquely identifies the chunk with respect to other chunks.
• the StripelD which is shared among the set of chunks, is used to associate the chunks with each other (since all of the chunks concern the same set of events).
• the set of IndexLocationIDs includes one IndexLocationID for each field value in the buffer/chunk.
• the IndexLocationID is used to access a field value in a different chunk that corresponds to the same event.
• the IndexLocationID includes the StripeID and an offset identifier (OffsetlD).
• OffsetID indicates which field value (within a buffer/chunk) corresponds to the desired event.
• the contents of the associated buffer 360 are compressed before they are stored in the chunk 350. Compressing the buffer contents makes this approach a cost-effective choice for long-term storage of data.
• the compressed version of the contents can be generated using any data compression algorithm.
• a column-type-specific compression algorithm is used.
• a column-based chunk that contains timestamps (such as the chunk associated with the ReceiptTime field) can use delta encoding.
• Delta encoding stores the difference relative to a previous value, rather than storing the value itself. For example, if the original value is a sequence of ⁇ 88888123, 88888125, 88888126, 88888127, 88888128>, then delta encoding would yield a sequence of ⁇ 88888123, 2, 1, 1, 1>.
• a low cardinality column can use common string compression.
• a unique string symbol table is generated. The index of the entry in the symbol table is stored, rather than the string itself.
• the chunk 350 also includes a "magic number" and a version identifier.
• the magic number sometimes called a file signature, is a short sequence of bytes that identifies the data type of the chunk. For example, the magic number is reasonably unique (i.e., unique with a high probability) across other data and file formats, including other chunks. Thus, when a chunk is read, it is easy to determine whether the chunk is in the expected format. If the chunk's actual magic number differs from the expected magic number, then the chunk is "wrong" (e.g., corrupted). The magic number thereby helps detect data corruption and resynchronize data chunk boundaries in order to recover corrupt data.
• the version identifier enables the accommodation of data and file formats that have changed. For example, when a chunk is read, the version identifier can be used in conjunction with the magic number to indicate additional information about the data or file format.
• control system 355 also generates a message digest of the contents of a buffer 360.
• the control system 355 applies a cryptographic hash function to the bytes stored in the buffer 360.
• Any cryptographic hash function can be used, such as Message-Digest algorithm 5 (MD5) or an algorithm in the Secure Hash Algorithm family (e.g., SHA-256).
• MD5 Message-Digest algorithm 5
• SHA-256 Secure Hash Algorithm family
• the digest value is stored in the chunk 350. This value can later be used to determine whether the buffer data that is stored in the chunk (in compressed form) has been changed or tampered with. This helps guarantee the integrity of stored events by making it noticeable when events have been changed.
• the digest value can be stored in the chunks table 380 along with the chunk's metadata. That way, if the chunk is later tampered with (or corrupted) while it is stored in a datafile 385, the message digest of the tampered chunk will not match the message digest that was previously stored in the chunks table 380.
• step 440 is performed ("triggered") when any one of the buffers 360 is full. In another embodiment, step 440 is performed (triggered) when a particular period of time (a "timeout window") has elapsed, during which no events were received by the event receiver 310.
• the control system 355 sends 450 the data chunks 350 to the storage manager 320.
• the storage manager 320 receives 460 the chunks 350.
• the control system 370 stores 470 the chunks in one or more datafiles 385 (see below). In one embodiment, a chunk is encrypted before it is stored for security purposes.
• the control system 370 also updates 470 the chunks table 380. For example, the control system 370 adds to the table information regarding the chunks 350 that it just stored in the datafile(s) 385.
• the control system 370 writes chunks 350 in "appending" order inside each datafile 385. This is sometimes referred to as "write-once journaled.”
• the control system maintains a "write pointer" that indicates a location within a datafile where a chunk can be written. After a chunk has been written to a datafile, the write pointer is modified to indicate a location within the same datafile (specifically, at the end of the chunk that was just written). If writing a chunk fills a datafile, the write pointer is modified to indicate a location within a different datafile (specifically, at the beginning) that can be used to store chunks.
• chunk writes are deferred by first caching chunks in memory. Multiple continuous chunks are then combined into one write operation in order to optimize full-stripe writes on RAID 5 disk storage systems. By using large sequential input operations such as writes, the hardware is driven at a high speed, throughput, and concurrency.
• the control system 370 uses the datafile and removes that datafile 's unique identifier from the free list (since that datafile is no longer available). If no pre-allocated datafile exists, the control system 370 creates a new one by locating available space and updating the datafiles table 375. For example, the control system 370 adds to the table information regarding the new datafile 385 that it just created. In one embodiment, the unique identifier assigned to the new datafile 385 is equal to the sum of 1 and the unique identifier associated with the datafile 385 that was most recently allocated. [0099] The method 400 has many desirable characteristics.
• the method 400 also features high availability, since it provides continuous access to data. Deleting old events does not fragment the storage medium, which means that no defragmentation process is required and therefore no maintenance window is required, either. Implicit downtime for cleanup tasks is not required. Also, since disk write operations are efficient, they avoid overhead in order to leave room for handling queries.
• EPS events-per-second
• FIG. 5 is a flowchart illustrating a method for reclaiming storage, according to one embodiment.
• the method 500 of FIG. 5 is performed by the storage manager 320 (e.g., its control system 370).
• the oldest datafile 385 associated with a particular retention policy (described below) is identified 510. Since datafiles have unique identifiers based on monotonically increasing numbers, it is easy to query the datafiles table 375 to find the oldest datafile (i.e., the datafile that has the lowest unique identifier) associated with the retention policy.
• a new entry is created 540 in the datafiles table 375, with a) a new unique identifier that is one higher than the highest used datafile identifier and b) a path attribute referring to the physical location of the previously oldest datafile (i.e., the datafile that was identified in step 510).
• the newly reclaimed datafile 385 is added 550 to the list of available pre-allocated datafiles and is ready to receive new chunks.
• a retention policy limits the retention of a chunk 350 based on, for example, a disk-space usage threshold or a maximum time to retain the chunk.
• Examples of when to execute the storage reclamation algorithm are: when all of the datafiles associated with that policy are full and no more datafiles can be allocated (e.g., because there is no storage space left); when a particular threshold has been reached (e.g., in terms of the amount of free storage space left for datafiles associated with that retention policy); when a particular period of time has elapsed; when a particular number of datafiles exist that are associated with that policy; and when the oldest chunk in a datafile associated with that policy has reached a threshold age.
• a datafile is backed up onto another system before its space is reclaimed. In this way, more storage can be made available while still maintaining existing data.
• all datafiles 385 are associated with the same retention policy.
• multiple retention policies exist, and each datafile is associated with any one of the multiple retention policies. Multiple datafiles can be associated with the same retention policy.
• a retention policy can be created and modified by a user.
• the storage manager 320 logically maintains one instance of the storage reclamation algorithm described above for each retention policy.
• each datafile 385 includes metadata that indicates the retention policy that applies to that datafile, and a chunk is stored in the datafile that corresponds to that chunk's retention policy.
• the system 170 shown in FIG. 3 is modified slightly (not shown).
• the event receiver 310 includes one set of buffers 360 and one metadata structure 365 for each retention policy.
• the control system 355 determines which retention policy should be applied to the event. This determination is based on, for example, a static mapping or an attribute of the particular event. Any attribute can be used, such as priority or event source. Based on this determination, the control system 355 stores the event field values in the appropriate set of buffers and updates the appropriate metadata structure. Thus, all event field values in a particular set of buffers will be associated with the same retention policy.
• the control system 370 determines the chunks' retention policy and stores the chunks in a datafile associated with that policy. Thus, all chunks in a particular datafile will be associated with the same retention policy.
• column-based chunks associated different buffers 360 can be associated with different retention policies, even if the buffers are storing field values from the same set of events. For example, chunks that store fields that are searched more often can have a different retention policy than chunks that store fields that are searched less often.
• a first field value from a first event could be associated with a first retention policy
• a second field value from the same event could be associated with a second (different) retention policy.
• each retention policy has its own group of datafiles 385. Each datafile is marked with a unique number. The number decides the order of the files within one group. The data files are written in appending order.
• Files are not updated, and files are written once and operated in append-only mode, which prevents log data tampering. As all files within one retention group are filled up, storage is reclaimed from the first (i.e., oldest) file in the group.
• a separate datafiles table 375 is maintained for each retention policy, which contains entries for datafiles 385 that have been allocated to that retention policy. If a free list is maintained, only one free list is used for the entire storage manager 320, regardless of how many retention policies exist.
• a row-based chunk contains complete information for a set of events.
• a column-based chunk of field values from that same set of events is a subset of the information contained in the row-based chunk. Since the column-based chunk contains less information than the row-based chunk, it is also faster to load into memory (e.g., from a datafile) and to search. Thus, if a search query term concerns the field of the column-based chunk, then it is faster to search the column- based chunk than to search the row-based chunk. Since the column-based chunk assists in searching, it is sometimes referred to as a "search index" or simply an "index.”
• a query can be executed by itself or as part of handling an interactive search or generating a report.
• a query is represented as an expression that can be evaluated against an event.
• the expression includes one or more search terms.
• a search term concerns the contents of an event, specifically, a particular field and the value of that field.
• the search term "Priority contains 'High'" concerns the Priority field and the value of that field being equal to '"High.”'.
• search term includes a timestamp field (e.g., EventReceipt) and a period of time (e.g., a start time and an end time).
• a timestamp field e.g., EventReceipt
• a period of time e.g., a start time and an end time.
• the query process occurs in multiple phases.
• the first phase filters data chunks 350 based on "field of interest" information (as stored in a chunk's meta-data).
• the second phase filters data chunks 350 based on field values (as stored in a chunk's "payload”).
• the third phase assembles the events that satisfy the query.
• the first phase thereby acts as a "rough cut” for identifying which data chunks (and their corresponding events) should be investigated further and which data chunks (and their corresponding events) should be ignored.
• the retention policy assigned to a chunk is not considered when events are queried or retrieved because it is not interesting which retention policy applies to a chunk that contains an event.
• search terms within the query are identified that concern information that was contained in the metadata structure 365 (back when the event field values were stored in the buffers 360 rather than as part of a data chunk 350 in a datafile 385).
• This metadata information includes the unique identifier of the associated event receiver and, for each field of interest, a minimum value and a maximum value that together reflect the range of values of that field over multiple events (initially, events whose field values are stored in the same buffer; later, events whose field values are stored in the same data chunk).
• the metadata information was transmitted to the storage manager 320 as part of a chunk 150. Then, the metadata information was stored in the chunks table 380.
• the "metadata search terms" are used to search the chunks table 380. This will yield which chunks (if any) could contain an event that satisfies the metadata search terms. In this way, a search can be constrained based on particular values (or ranges of values) for event receiver and/or fields of interest (since these values are stored in the metadata in the chunks table 380).
• the data chunks 350 identified by the first phase are further filtered based on field values (as stored in a chunk's "payload").
• search terms within the query are identified that concern fields whose values are stored in a column-based chunk 350 (i.e., "indexed" fields). For example, if a search term concerns the SourceIP Address field, then a column- based chunk that is associated with the SourceIPAddress field is identified. (This can be done by examining the chunk's IndexID.)
• the requested value of the search term e.g., a particular IP address
• the payload portion of the chunk i.e., the set of field values
• the events that satisfy the query are assembled.
• a particular column-based chunk has been identified that is associated with the SourceIPAddress field.
• a particular field value entry has been identified within the chunk as matching the requested value of the search term. That particular field value entry is associated with an IndexLocationID.
• the IndexLocationID is now used to obtain the remaining field values of the event.
• the IndexLocationID includes a StripeID and an OffsetlD.
• the StripeID is used to identify other column-based chunks that concern the same set of events. (In one embodiment, the storage manager 320 maintains a mapping of StripeID to list of ChunkIDs associated with that StripelD.) Once those other column-based chunks are identified, the appropriate field values (i.e., those field values that belong to the same event as the event identified based on the SourceIPAddress field) are obtained using the OffsetID.
• DeviceVendor, TransportProtocol, and Priority are not "fields of interest" (and thus do not have value ranges stored in the metadata portions of any chunks).
• One way to execute this query is as follows: 1) Identify column-based chunks associated with the TransportProtocol field. Search those chunks for field values equal to "TCP.” For each matching field value, store the associated IndexLocationID.
• the events are analyzed in a particular order.
• the events are analyzed based on their event receipt time, in either ascending order (i.e., oldest events first) or descending order (newest events first). Analyzing the events in a particular order and appending matching events to the search results means that the events in the search results will already be in that particular order. No sorting of the events is required.
• the above algorithm searches for event field values that are stored in chunks 350.
• the logging system 170 may contain additional event field values in the event receiver 310 (e.g., within the set of buffers 360) that have not yet been stored in a chunk.
• the algorithm above will not search these event field values.
• the set of buffers 360 is flushed so that the event field values will be sent to the storage manager 320 and stored in a chunk. This way, when the algorithm is executed, the event field values that were formerly in the set of buffers will be searched also.
• a separate search is executed on the event receiver 310 using the contents of the metadata structure 365 and the set of buffers 360, similar to the algorithm described above. This way, all event field values will be searched, whether they are stored in the storage manager 320 or in the event receiver 310.
• FIG. 6 is a flowchart illustrating a method for querying, according to one embodiment.
• the method 600 of FIG. 6 is performed by the storage manager 320 (e.g., its control system 370). Before the method 600 begins, a search query is received.
• the search query includes one or more search terms.
• Any metadata search terms (within the received search query) are identified 610.
• the identified metadata search terms are used to search 620 the chunks table 380. Recall that each entry in the chunks table 380 corresponds to a chunk 350, and an entry includes the metadata stored in the chunk and the location of the chunk. The identified metadata search terms are used to search the metadata portion of the chunks table 380.
• Each chunk 350 whose metadata satisfies the metadata search terms is retrieved 630 using the location of the chunk, which was stored in the chunks table 380.
• Any indexed search terms (within the received search query) are identified 640.
• Any chunks (from among those retrieved in step 630) associated with the indexed search terms are identified 650.
• the identified indexed search terms are used to search 660 the payload portions of the chunks that were identified in step 640.
• Events that satisfy the search query are assembled 670.
• the entry's IndexLocationID is determined and used to access the field value entries of the remaining fields of the matching event.
• the logging system 170 supports archiving functionality for datafiles 385.
• a datafile 385 can be imported into and exported out of the logging system 170.
• a datafile 385 can be backed up onto another system and later restored into the logging system 170. Since events are stored in chunks and chunks are stored in datafiles, events are easily transferable to nearline or offline storage.
• a datafile is archived automatically based on archival criteria, which can be similar to the criteria that are used for querying (e.g., values of information stored in metadata structures of chunks within the datafile).
• a datafile is archived manually (e.g., in response to a user command).
• the '078 Application describes storing event data using only row-based chunks.
• the present application describes storing event data using only column-based chunks.
• a third type of event storage uses both row-based chunks and column-based chunks. This type of event storage stores an event twice - once using a row-based chunk and once using one or more column-based chunks. For example, a set of events would be stored as one row-based chunk. The field values of those events would also be stored as column-based chunks (one column-based chunk for each field).
• row-based storage is write-optimized, while column-based storage is read- optimized. The advantage to storing an event using both row-based and column-based chunks is that both of these optimizations are available.
• the row-based chunk is faster to write, so using that architecture enables an event to be stored more quickly.
• the column-based chunk is faster to read, so using that architecture enables an event to be read (e.g., queried) more quickly.
• the generation and storage of the row-based chunk and the generation and storage of the column-based chunks are not performed as part of the same transaction. If events are being received at a very high rate, then the generation and storage of column-based chunks ("indexing") can lag behind the generation and storage of row-based chunks. No data is dropped or delayed to the cost (e.g., time cost) of indexing.
• the row-based chunks and the column-based chunks can be associated with different retention policies. For example, a row-based chunk that stores a set of events can be associated with a first retention policy, and the column-based chunks that store the same set of events can be associated with a second retention policy (or multiple retention policies, as described above). As long as the row- based chunk exists, then the column-based chunks can be recreated if necessary. Likewise, as long as the column-based chunks exist, then the row-based chunk can be recreated if necessary. In general, less space is required to store a set of events in multiple column-based chunks than in one row-based chunk. So, in one embodiment, column-based chunks are stored longer than row-based chunks (e.g., for the same set of events).
• the events don't have to be extracted from a stored row-based chunk and loaded into memory later.
• Another possibility is for the event receiver to create the column-based chunks, as described above. Or, the log data that is sent to the event receiver could already be in column-based format.
• the four storage types described above are not mutually exclusive.
• One logging system can use all four storage types. For example, a first set of events can be stored using a row-based chunk, a second set of events can be stored using column-based chunks, a third set of events can be stored using both row- based and column-based chunks (for all columns), and a fourth set of events can be stored using both row-based and column-based chunks (for selected columns),.
• Which storage strategy is best depends on the circumstances. While row-based chunks are faster to create, column-based chunks are faster to query.
• the storage architecture is chosen based on when an event was received. For example, events that were received recently (such as within the past 30 days) are stored using both row-based and column-based chunks (for all columns). Older events are stored using only row-based chunks (or only column-based chunks). If the older events were previously stored using both row-based and column-based chunks (for all columns), then the row-based chunks and the column-based chunks contain the same information, so either can be deleted. If the older events were previously stored using both row-based and column-based chunks (for selected columns), then the row-based chunks and the column-based chunks do not contain the same information, and deleting the row-based chunks will cause information to be lost. In this situation, it might be better to delete the column-based chunks (since the information that they contain is redundant).
• the storage architecture is chosen based on the event receiver that received the event. For example, events that were received by a first receiver are stored using both row-based and column-based chunks. Events that were received by a second receiver are stored using only row-based chunks (or only column-based chunks).
• initialization of the logging system 170 includes specifying a storage strategy (e.g., row-only, column-only, row-and-all-columns, or row-and-selected-columns) and when that strategy should be used (e.g., based on event receipt time falling within a time period or based on event being received by a particular event receiver).
• a storage strategy e.g., row-only, column-only, row-and-all-columns, or row-and-selected-columns
• the storage strategy can be changed at any time.
• the '078 Application describes querying and data retrieval for event data stored using only row-based chunks.
• data chunks are identified that could contain an event that satisfies the query.
• search terms within the query are identified that contain information that was contained in the metadata structure.
• the "metadata search terms" are used to search the chunks table. In this way, a search can be constrained based on particular values for information that was stored in the metadata.
• the identified chunks are disassembled into their constituent events. Events that satisfy the query are identified.
• the present application describes querying and data retrieval for event data stored using only column-based chunks.
• data chunks are first filtered based on "field of interest" information (as stored in a chunk's metadata).
• the remaining chunks are then filtered based on field values (as stored in a chunk's "payload").
• the events that satisfy the query are assembled field-by-field.
• a column-based chunk includes a set of index location identifiers (IndexLocationIDs).
• the set of IndexLocationIDs includes one IndexLocationID for each field value in the chunk.
• the IndexLocationID is used to access a field value in a different chunk that corresponds to the same event.
• the IndexLocationID is used to assemble events (field-by-field) that satisfy a search query.
• a column-based chunk indirectly references its associated row-based chunk using a "table location identifier" (TableLocationID).
• TableLocationID a set of table location identifiers (TableLocationIDs) is stored as its own column-based chunk. Each TableLocationID in the chunk corresponds to a particular event.
• the TableLocationID includes a row-based chunk identifier (RBChunkID) and a row-based chunk offset identifier (RBChunkOffsetlD).
• the RBChunkID indicates which row-based chunk contains the event associated with the TableLocationID.
• the RBChunkOffsetlD indicates where (within the row-based chunk) that event begins. Whenever a set of column-based chunks is generated (e.g., based on an existing row- based chunk or by an event receiver as described above with respect to step 440), a TableLocationID column-based chunk is also generated. Later, when a query is performed and a matching field value is found in one of these column-based chunks, the associated IndexLocationID (specifically, the OffsetlD) is used to access the appropriate TableLocationID from the TableLocationID column-based chunk.
• the OffsetlD is used to access the appropriate TableLocationID from the TableLocationID column-based chunk.
• a query optimizer determines which execution strategy should be used for a particular query. Specifically, the query optimizer calculates a "total cost" for each execution strategy and then selects the strategy with the lowest cost. (In one embodiment, the query optimizer considers only the column-only strategy and the row-and-column strategy, since the row-only strategy is likely to be the highest in cost.)
• the total cost of an execution strategy is a function of different sub- costs, such as the CPU cost and the input/output (I/O) cost.
• the sub-costs are functions of the selectivity of the query's predicates and the number of columns (fields) involved in the query (both the predicates and the desired search results).
• the selectivity is estimated based on statistical information on fields of past events. For example, information on the data distribution on a field is provided by a histogram, which divides the values on a field into A: buckets.
• a search query might not request all of the fields of events that satisfy the query.
• the event can be obtained in its entirety as described above (using the TableLocationID and row-based chunk) and then unnecessary fields can be removed before generating the search results. If the search query requests many fields, then this approach might be faster than obtaining each field separately from a different column-based chunk.
• searching a column-based chunk is faster than searching a row-based chunk.
• a search query term that concerns a particular event field. If that field that is associated with an existing column-based chunk, then that column-based chunk is searched for the desired field value. If no such column-based chunk exists, then the appropriate row-based chunk is searched instead.
• the column-based chunk can be created (based on the row-based chunk) and then searched. Creating the column-based chunk might be preferable when the same field will need to be searched for several events.
• the column-based chunk generator supports checkpoint recovery. Specifically, in case of system crash, the indexer can recover and resume from the last checkpoint.
• the checkpoint interval is configurable. The longer the interval, the higher the performance of the indexer (e.g., the higher the speed with which column-based chunks are generated), but the longer the crash recovery time.
• the indexer persists the last-scanned TableLocationID and the last- created IndexLocationID.
• the indexer starts at the persisted TableLocationID and IndexLocationID and continues to index any remaining data in the table.
• Archiving can be performed on row-based storage and/or on column-based storage. Archival criteria for automatic archiving can differ between the row-based storage and the column- based storage.

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• 2009
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